Index

  1. Data
  2. Method (explained based on LM2)
  3. Results for other cases

Data

source('../../workflow/resources/annotateVariants.R')
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sampleName <- 'Br11'
inputFolder <- '/cluster/work/bewi/members/jgawron/projects/CTC/input_folder'

Mutation distance matrix

For each cluster (defined by color), we computed a pairwise distance for each mutation pair that indicates how often the two mutations occur in the same private branch of cells from the cluster:

dist(M1, M2) = 0 (for M1 = M2)
dist(M1,M2) = 1 - (%samples where M1 and M2 are both in the same private branch of a cell from the cluster) (elsewise)

A private branch is defined as the path from a leaf to the node just below the LCA of this leaf to another leaf from the same cluster.

This is a generalization of the earlier method to find the top seperating mutations of pairs of leafs. The generalization was necessary to handle the larger clusters that were broken in more than 2 pieces.

clusterName <- 'lightcoral'

d <- read.table(file.path(inputFolder, sampleName, paste0(sampleName, '_postSampling_',clusterName,'.txt') ),header=TRUE,sep="\t", stringsAsFactors=F, row.names=1)
mat<-as.matrix(d)
mat[1:4, 1:4]
##                chr6_32293617 chr5_139276028 chr7_65399410 chr3_121257206
## chr6_32293617       0.000000       0.964925      0.999925       0.954200
## chr5_139276028      0.964925       0.000000      1.000000       0.982775
## chr7_65399410       0.999925       1.000000      0.000000       1.000000
## chr3_121257206      0.954200       0.982775      1.000000       0.000000

Position-wise coverage score

For each position, we computed the percentage of samples that have a coverage of at least 3 at this position. This is meant as a simple score of the data quality of a position that can be used in addition to the separation score to pick mutations for the wet lab experiments. Furthermore, we added simple functional annotations to the variants.

coverage<-read.table(file.path(inputFolder, sampleName, paste(sampleName, 'covScore.txt', sep = '_')),header=TRUE,sep="\t", stringsAsFactors=F, row.names=1)
coverage$variantName <- rownames(coverage)
head(coverage)
##                 covScore    variantName
## chr6_32293617  0.7222222  chr6_32293617
## chr5_139276028 0.5000000 chr5_139276028
## chr7_65399410  0.6666667  chr7_65399410
## chr3_121257206 0.5555556 chr3_121257206
## chrX_290695    0.2777778    chrX_290695
## chr1_109573768 0.3888889 chr1_109573768
annotations <- annotate_variants(sampleName, inputFolder)

coverage <- inner_join(coverage, annotations, by = "variantName")

Method

Mutation clustering

  1. Overview: Raw plot of the distance matrix.
  2. Filter distant mutations: Remove all mutations that are not close to any other mutations (minDist>0.5)
  3. Dendrogram: Use the distance matrix to cluster the mutations using hierarchical clustering.
  4. Cluster remaining mutations: Re-do the hierarchical clustering witht the remaining mutations
  5. Define cut point to get about as many groups as there are cluster pieces
  6. Rank top separating mutations: Within each group, reduce distance matrix to mutations in the group, rank them by their average distance to other mutations in the group.

###Overview To get an overview, we plot the full distance matrix:

library(heatmaply)
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heatmaply(mat)

Filter out distant mutations

mat2 <- mat
diag(mat2) <- 1
min_dist <- apply(mat2, 1, min) # find minimum distance to other mutations
selected_muts <- which(min_dist<0.9) # select those below 0.5 say
mat2 <- mat[selected_muts, selected_muts]

This is what the distance matrix looks like now:

heatmaply(mat2)
coverage %>% filter(variantName %in% colnames(mat2))
##     covScore    variantName REF ALT relevant
## 1  0.7222222  chr6_32293617   T   A MODERATE
## 2  0.3888889 chr1_109573768   G   A     NONE
## 3  0.5555556  chr6_83656797   G   C MODERATE
## 4  0.3888889 chr4_133162921   A   C     NONE
## 5  0.6111111 chrX_141698461   T   G     NONE
## 6  0.3888889 chr16_12002913   G   A     NONE
## 7  0.5000000 chr17_12133785   G   A     NONE
## 8  0.3888889 chr2_218644549   A   C MODERATE
## 9  0.5000000  chr18_7002377   G   A     NONE
## 10 0.7777778 chr3_100825799   G   A MODERATE
## 11 0.2222222 chr10_44931932   A   T     NONE
## 12 0.5555556 chr3_121490363   A   G     NONE
## 13 0.6666667 chr14_19499672   G   A     NONE
## 14 0.7222222 chr14_19499787   T   G     NONE
## 15 0.5555556 chr6_111372656   G   C MODERATE
## 16 0.4444444 chr6_112087431   T   G MODERATE
## 17 0.2222222  chr3_10358774   G   A MODERATE
## 18 0.3888889 chr4_146503823   T   G     NONE
## 19 0.8333333  chr3_75669309   C   G     NONE
## 20 0.6666667  chr6_30951754   T   C MODERATE
## 21 0.4444444 chr6_157174912   A   G     NONE
## 22 0.2777778  chr6_35080094   C   A     NONE
## 23 0.3333333  chr20_2462660   G   A MODERATE
## 24 0.2777778 chr22_43928750   A   G     NONE

Dendrogram of the remaining mutations

To cluster mutations, we create a dendrogram based on the pairwise distances:

d_mat <- as.dist(mat)
hc <- hclust(d_mat, "average")                   ## hierarchical clustering of mutations based on distance matrix
par(cex=0.6)
plot(hc, main = "Dendrogram based on average pairwise distance", sub = "", xlab = "Separating mutations")

No apparent clustering visible.